A fundamental business problem is the inability to make effective use of stored data that a company collects. The data is often stored in one or more databases that are maintained by the business. One of the most valuable assets of a business is its cumulative industry experience. As a result of its experience, the business has the ability learn from prior mistakes so that it can be more efficient. To survive, the business must leverage its experience to maintain a competitive advantage. The efficiency of the business is reflected in higher margins, higher sales and/or more competitive prices.
Businesses often have too much data and not enough information. Businesses tend to repeat the same costly mistakes because of faulty assumptions. Oftentimes business managers are unable to validate their assumptions because the underlying information, while being kept by the business, is too difficult to obtain in a useable format. Despite the recent improvements in hardware and database software, most medium to large businesses fail to provide access to the business data. These businesses also lack the ability to manipulate the business data beyond the existing report formats. The inability to easily modify the existing formats often leads to uninformed and/or one-sided decision making that can cause expensive mistakes.
Typically, a business user develops a theory about a problem or ways to improve the business and desires to confirm the theory. The business user submits a request for a new business report to the information technology (IT) personnel. Oftentimes, it takes the IT personnel several weeks or months to fulfill the request. After reviewing the new report, the business user realizes that a different report is needed. Alternatively, the business user has additional questions that are not answered by the new report. The business user returns to the IT personnel and makes another request for a new report. Several weeks or months later, the IT personnel provide a revised report. This process continues for several iterations until the business user is satisfied, the business user gives up, or the IT personnel refuse to create additional reports. Because of the time delays that are involved, the business user often gives up trying to validate theories relating to problems or potential ways to improve the business. Alternatively, the business user implements an untested theory with potentially negative results.
Most business analytical systems provide interactive access to subsets of the data within an enterprise. These business analytical systems typically require the intervention of IT personnel to create or change the reports. Most of the time, the reports are not presented in a fashion that can be comprehended by a typical business user having spreadsheet skills. The analytical systems also typically require predefined queries. In addition, these analytical systems require the user to perform data manipulation, computation and display using a data investigation program and to use a different report preparation and dissemination program.
Businesses keep their data in data stores such as legacy systems, data warehouses, data cubes and data marts. Each of these types of data stores performs some of the desired functions. However, all of the conventional data stores have fundamental flaws. None of the conventional data stores provide a broad, flexible system for allowing business users to access all of the data within the organization.
Legacy systems were typically written using older database technology. The legacy systems are maintained because they still adequately serve their specific, original purposes. Many of these legacy systems originated at a time when data systems served very specific and limited business purposes. The legacy systems do not meet the needs of the recently developed, general-purpose business analytical systems. The legacy systems do not attempt to address the type of flexible business analysis that businesses need to remain competitive.
Data warehouses were developed to consolidate data storage. Data warehouses are fed by multiple databases and other data sources. The data from these sources is consolidated into one data store. Data warehouses are typically implemented in commercial relational database management systems, such as Oracle®.
Data warehousing held out the promise of being the model for general-purpose business analysis. Data warehouses collect all of the relevant data from various systems, databases and other data sources in one place. Data warehouses have advantages over prior systems that are distributed across multiple, incompatible legacy systems. Data warehouses, however, fail to provide an adequate business analysis tool. The data structures of a typical corporate data warehouse are much too complex for a typical business user to understand. As data warehouses grow, the response time for reporting purposes is too slow to qualify as “interactive time”. As a result, the business user is now as dependent as ever on the IT personnel to define, create, modify, and maintain database access and report generation programs. As previously discussed above, this is an untenable situation because the business user does not know in advance what data and report formats that he or she will need to see.
Data cubes and data marts were created to address user problems relating to accessibility of data in data warehouses. Data cubes and data marts address the structural complexity of the data structures and the slow response times. Data cubes and data marts create either aggregates (so-called dimensional databases) and/or subsets of a full database that are accessed through a user-friendly interface. Many also include excellent business analysis tools for data manipulation and display. The data cubes and data marts do not however, contain all of the data of the business. The data cubes and data marts contain aggregates and/or subsets of the underlying database and can only respond to a limited set of queries that relate to the selected subsets of data.
In practice, the IT personnel and the business users need to spend a considerable amount of time designing the data marts and data cubes and discussing the content and structure of the cubes. Oftentimes, because the IT personnel lack sufficient knowledge of the business, the IT personnel have a difficult time understanding what the business user needs. Despite the planning time, the business user has difficulty anticipating the business questions that he or she is going to ask prior to having useable access to the data. This situation often results in several versions of the data cubes and data marts. Between the versions, however, are significant organizational delays. The time delays are exacerbated by the rapidly changing state of available data and associated business metrics. In practice, the data cubes and data marts are being overwhelmed by the dynamism of business change, which is fueled by the competitive business environment and the explosion of electronic commerce.
Therefore, a business analysis system that provides unlimited access to all of the business data within “interactive time” would be desirable. The business analysis system should also allow business users with moderate skill to work independently of IT personnel. The business analysis system should also provide manipulation, computation and display tools.